Overview

Dataset statistics

Number of variables15
Number of observations6497
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory761.5 KiB
Average record size in memory120.0 B

Variable types

NUM13
CAT2

Warnings

citric_acid has 151 (2.3%) zeros Zeros

Reproduction

Analysis started2020-11-21 05:16:30.520853
Analysis finished2020-11-21 05:17:28.746116
Duration58.23 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct4898
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2042.535632
Minimum0
Maximum4897
Zeros2
Zeros (%)< 0.1%
Memory size50.8 KiB
2020-11-21T13:17:29.162115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile162
Q1812
median1649
Q33273
95-th percentile4572.2
Maximum4897
Range4897
Interquartile range (IQR)2461

Descriptive statistics

Standard deviation1436.926393
Coefficient of variation (CV)0.7035012612
Kurtosis-1.11588531
Mean2042.535632
Median Absolute Deviation (MAD)1100
Skewness0.4104190593
Sum13270354
Variance2064757.459
MonotocityNot monotonic
2020-11-21T13:17:29.719118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02< 0.1%
 
11422< 0.1%
 
10902< 0.1%
 
10942< 0.1%
 
10982< 0.1%
 
11022< 0.1%
 
11062< 0.1%
 
11102< 0.1%
 
11142< 0.1%
 
11182< 0.1%
 
Other values (4888)647799.7%
 
ValueCountFrequency (%) 
02< 0.1%
 
12< 0.1%
 
22< 0.1%
 
32< 0.1%
 
42< 0.1%
 
ValueCountFrequency (%) 
48971< 0.1%
 
48961< 0.1%
 
48951< 0.1%
 
48941< 0.1%
 
48931< 0.1%
 

fixed_acidity
Real number (ℝ≥0)

Distinct106
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.215307065
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:30.277122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.7
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.296433758
Coefficient of variation (CV)0.1796782516
Kurtosis5.061160665
Mean7.215307065
Median Absolute Deviation (MAD)0.6
Skewness1.723289647
Sum46877.85
Variance1.680740488
MonotocityNot monotonic
2020-11-21T13:17:30.967120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.83545.4%
 
6.63275.0%
 
6.43054.7%
 
72824.3%
 
6.92794.3%
 
7.22734.2%
 
6.72644.1%
 
7.12574.0%
 
6.52423.7%
 
7.42383.7%
 
Other values (96)367656.6%
 
ValueCountFrequency (%) 
3.81< 0.1%
 
3.91< 0.1%
 
4.22< 0.1%
 
4.43< 0.1%
 
4.51< 0.1%
 
ValueCountFrequency (%) 
15.91< 0.1%
 
15.62< 0.1%
 
15.52< 0.1%
 
152< 0.1%
 
14.31< 0.1%
 

volatile_acidity
Real number (ℝ≥0)

Distinct187
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3396659997
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:31.623119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.29
Q30.4
95-th percentile0.67
Maximum1.58
Range1.5
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1646364741
Coefficient of variation (CV)0.4847010717
Kurtosis2.825372417
Mean0.3396659997
Median Absolute Deviation (MAD)0.08
Skewness1.495096542
Sum2206.81
Variance0.0271051686
MonotocityNot monotonic
2020-11-21T13:17:32.156124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.282864.4%
 
0.242664.1%
 
0.262563.9%
 
0.252383.7%
 
0.222353.6%
 
0.272323.6%
 
0.232213.4%
 
0.22173.3%
 
0.32143.3%
 
0.322053.2%
 
Other values (177)412763.5%
 
ValueCountFrequency (%) 
0.0840.1%
 
0.0851< 0.1%
 
0.091< 0.1%
 
0.160.1%
 
0.10560.1%
 
ValueCountFrequency (%) 
1.581< 0.1%
 
1.332< 0.1%
 
1.241< 0.1%
 
1.1851< 0.1%
 
1.181< 0.1%
 

citric_acid
Real number (ℝ≥0)

ZEROS

Distinct89
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3186332153
Minimum0
Maximum1.66
Zeros151
Zeros (%)2.3%
Memory size50.8 KiB
2020-11-21T13:17:32.638126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.25
median0.31
Q30.39
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.1453178649
Coefficient of variation (CV)0.4560662791
Kurtosis2.397239216
Mean0.3186332153
Median Absolute Deviation (MAD)0.07
Skewness0.4717306725
Sum2070.16
Variance0.02111728186
MonotocityNot monotonic
2020-11-21T13:17:33.063119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.33375.2%
 
0.283014.6%
 
0.322894.4%
 
0.492834.4%
 
0.262574.0%
 
0.342493.8%
 
0.292443.8%
 
0.272363.6%
 
0.242323.6%
 
0.312303.5%
 
Other values (79)383959.1%
 
ValueCountFrequency (%) 
01512.3%
 
0.01400.6%
 
0.02560.9%
 
0.03320.5%
 
0.04410.6%
 
ValueCountFrequency (%) 
1.661< 0.1%
 
1.231< 0.1%
 
160.1%
 
0.991< 0.1%
 
0.912< 0.1%
 

residual_sugar
Real number (ℝ≥0)

Distinct316
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.443235339
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:34.110118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.2
Q11.8
median3
Q38.1
95-th percentile15
Maximum65.8
Range65.2
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.757803743
Coefficient of variation (CV)0.8740764355
Kurtosis4.359271948
Mean5.443235339
Median Absolute Deviation (MAD)1.7
Skewness1.435404263
Sum35364.7
Variance22.63669646
MonotocityNot monotonic
2020-11-21T13:17:34.480119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22353.6%
 
1.82283.5%
 
1.62233.4%
 
1.42193.4%
 
1.21953.0%
 
2.21872.9%
 
2.11792.8%
 
1.91762.7%
 
1.71752.7%
 
1.51722.6%
 
Other values (306)450869.4%
 
ValueCountFrequency (%) 
0.62< 0.1%
 
0.770.1%
 
0.8250.4%
 
0.9410.6%
 
0.9540.1%
 
ValueCountFrequency (%) 
65.81< 0.1%
 
31.62< 0.1%
 
26.052< 0.1%
 
23.51< 0.1%
 
22.61< 0.1%
 

chlorides
Real number (ℝ≥0)

Distinct214
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05603386178
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:35.086118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.065
95-th percentile0.102
Maximum0.611
Range0.602
Interquartile range (IQR)0.027

Descriptive statistics

Standard deviation0.03503360137
Coefficient of variation (CV)0.6252219686
Kurtosis50.89805146
Mean0.05603386178
Median Absolute Deviation (MAD)0.011
Skewness5.399827732
Sum364.052
Variance0.001227353225
MonotocityNot monotonic
2020-11-21T13:17:35.403117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0442063.2%
 
0.0362003.1%
 
0.0421872.9%
 
0.0461852.8%
 
0.0481822.8%
 
0.051822.8%
 
0.041822.8%
 
0.0471752.7%
 
0.0451742.7%
 
0.0341692.6%
 
Other values (204)465571.6%
 
ValueCountFrequency (%) 
0.0091< 0.1%
 
0.0123< 0.1%
 
0.0131< 0.1%
 
0.01440.1%
 
0.01540.1%
 
ValueCountFrequency (%) 
0.6111< 0.1%
 
0.611< 0.1%
 
0.4671< 0.1%
 
0.4641< 0.1%
 
0.4221< 0.1%
 

free_sulfur_dioxide
Real number (ℝ≥0)

Distinct135
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.52531938
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:35.733119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median29
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.74939977
Coefficient of variation (CV)0.5814648342
Kurtosis7.906238067
Mean30.52531938
Median Absolute Deviation (MAD)12
Skewness1.220066074
Sum198323
Variance315.0411923
MonotocityNot monotonic
2020-11-21T13:17:36.213118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
291832.8%
 
61702.6%
 
261612.5%
 
151572.4%
 
241522.3%
 
311522.3%
 
171492.3%
 
341462.2%
 
351442.2%
 
231422.2%
 
Other values (125)494176.1%
 
ValueCountFrequency (%) 
13< 0.1%
 
22< 0.1%
 
3590.9%
 
4520.8%
 
51292.0%
 
ValueCountFrequency (%) 
2891< 0.1%
 
146.51< 0.1%
 
138.51< 0.1%
 
1311< 0.1%
 
1281< 0.1%
 

total_sulfur_dioxide
Real number (ℝ≥0)

Distinct276
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.7445744
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:36.599122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q177
median118
Q3156
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.52185452
Coefficient of variation (CV)0.488332648
Kurtosis-0.3716636549
Mean115.7445744
Median Absolute Deviation (MAD)39
Skewness-0.001177478234
Sum751992.5
Variance3194.720039
MonotocityNot monotonic
2020-11-21T13:17:36.895120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
111721.1%
 
113651.0%
 
122570.9%
 
117570.9%
 
124560.9%
 
128560.9%
 
98560.9%
 
114560.9%
 
118550.8%
 
119540.8%
 
Other values (266)591391.0%
 
ValueCountFrequency (%) 
63< 0.1%
 
740.1%
 
8140.2%
 
9150.2%
 
10280.4%
 
ValueCountFrequency (%) 
4401< 0.1%
 
366.51< 0.1%
 
3441< 0.1%
 
3131< 0.1%
 
307.51< 0.1%
 

density
Real number (ℝ≥0)

Distinct998
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9946966338
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:37.291121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9899
Q10.99234
median0.99489
Q30.99699
95-th percentile0.999392
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00465

Descriptive statistics

Standard deviation0.002998673004
Coefficient of variation (CV)0.003014660854
Kurtosis6.606066991
Mean0.9946966338
Median Absolute Deviation (MAD)0.00231
Skewness0.5036017301
Sum6462.54403
Variance8.992039783e-06
MonotocityNot monotonic
2020-11-21T13:17:37.752117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9976691.1%
 
0.9972691.1%
 
0.992641.0%
 
0.998641.0%
 
0.9928631.0%
 
0.9986610.9%
 
0.9966590.9%
 
0.9962590.9%
 
0.9956550.8%
 
0.9968550.8%
 
Other values (988)587990.5%
 
ValueCountFrequency (%) 
0.987111< 0.1%
 
0.987131< 0.1%
 
0.987221< 0.1%
 
0.98741< 0.1%
 
0.987422< 0.1%
 
ValueCountFrequency (%) 
1.038981< 0.1%
 
1.01032< 0.1%
 
1.003692< 0.1%
 
1.00321< 0.1%
 
1.003153< 0.1%
 

pH
Real number (ℝ≥0)

Distinct108
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.218500847
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:38.113115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.97
Q13.11
median3.21
Q33.32
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.1607872021
Coefficient of variation (CV)0.04995717254
Kurtosis0.3676572674
Mean3.218500847
Median Absolute Deviation (MAD)0.11
Skewness0.3868387981
Sum20910.6
Variance0.02585252436
MonotocityNot monotonic
2020-11-21T13:17:38.514118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.162003.1%
 
3.141933.0%
 
3.221852.8%
 
3.21762.7%
 
3.191702.6%
 
3.151702.6%
 
3.181682.6%
 
3.241612.5%
 
3.121542.4%
 
3.11542.4%
 
Other values (98)476673.4%
 
ValueCountFrequency (%) 
2.721< 0.1%
 
2.742< 0.1%
 
2.771< 0.1%
 
2.793< 0.1%
 
2.83< 0.1%
 
ValueCountFrequency (%) 
4.012< 0.1%
 
3.92< 0.1%
 
3.851< 0.1%
 
3.821< 0.1%
 
3.811< 0.1%
 

sulphates
Real number (ℝ≥0)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5312682777
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:38.969123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1488058736
Coefficient of variation (CV)0.2800955372
Kurtosis8.653698823
Mean0.5312682777
Median Absolute Deviation (MAD)0.08
Skewness1.797270004
Sum3451.65
Variance0.02214318802
MonotocityNot monotonic
2020-11-21T13:17:39.476125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.52764.2%
 
0.462433.7%
 
0.542353.6%
 
0.442323.6%
 
0.382143.3%
 
0.482083.2%
 
0.522033.1%
 
0.491973.0%
 
0.471912.9%
 
0.451902.9%
 
Other values (101)430866.3%
 
ValueCountFrequency (%) 
0.221< 0.1%
 
0.231< 0.1%
 
0.2540.1%
 
0.2640.1%
 
0.27130.2%
 
ValueCountFrequency (%) 
21< 0.1%
 
1.981< 0.1%
 
1.952< 0.1%
 
1.621< 0.1%
 
1.611< 0.1%
 

alcohol
Real number (ℝ≥0)

Distinct111
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.49180083
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:39.840120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.3
Q311.3
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.192711749
Coefficient of variation (CV)0.1136803651
Kurtosis-0.5316873829
Mean10.49180083
Median Absolute Deviation (MAD)0.9
Skewness0.5657177291
Sum68165.23
Variance1.422561316
MonotocityNot monotonic
2020-11-21T13:17:40.085140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.53675.6%
 
9.43325.1%
 
9.22714.2%
 
102293.5%
 
10.52273.5%
 
112173.3%
 
92153.3%
 
9.82143.3%
 
10.41943.0%
 
9.31933.0%
 
Other values (101)403862.2%
 
ValueCountFrequency (%) 
82< 0.1%
 
8.450.1%
 
8.5100.2%
 
8.6230.4%
 
8.7801.2%
 
ValueCountFrequency (%) 
14.91< 0.1%
 
14.21< 0.1%
 
14.051< 0.1%
 
14120.2%
 
13.93< 0.1%
 

quality
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.818377713
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Memory size50.8 KiB
2020-11-21T13:17:40.519114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8732552715
Coefficient of variation (CV)0.1500856965
Kurtosis0.2323222693
Mean5.818377713
Median Absolute Deviation (MAD)1
Skewness0.1896226934
Sum37802
Variance0.7625747693
MonotocityNot monotonic
2020-11-21T13:17:40.692121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
6283643.7%
 
5213832.9%
 
7107916.6%
 
42163.3%
 
81933.0%
 
3300.5%
 
950.1%
 
ValueCountFrequency (%) 
3300.5%
 
42163.3%
 
5213832.9%
 
6283643.7%
 
7107916.6%
 
ValueCountFrequency (%) 
950.1%
 
81933.0%
 
7107916.6%
 
6283643.7%
 
5213832.9%
 

quality_level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.8 KiB
Good
4974 
Excellent
1277 
Bad
 
246
ValueCountFrequency (%) 
Good497476.6%
 
Excellent127719.7%
 
Bad2463.8%
 
2020-11-21T13:17:40.970145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-21T13:17:41.149121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:41.401123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length4
Mean length4.944897645
Min length3

wine_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.8 KiB
white
4898 
red
1599 
ValueCountFrequency (%) 
white489875.4%
 
red159924.6%
 
2020-11-21T13:17:41.667121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-21T13:17:41.824140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:42.454126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length4.507772818
Min length3

Interactions

2020-11-21T13:16:38.170094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:38.471117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:38.702120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:38.941095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:39.173096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:39.421113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:39.655121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:39.888118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:40.116114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:40.351116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:40.591400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:40.824400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:41.067399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:41.352404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:41.726404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:41.965404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:42.221400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:42.491424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:42.736399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:42.980401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:43.233402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:43.467404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:43.705401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:43.946403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:44.192404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:44.461406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:44.789401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:45.038423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:45.302401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:45.558398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:45.849403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:46.115405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:46.373420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:46.623405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:46.853406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:47.106798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:47.362301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:47.627301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:47.953306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:48.208294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:48.442302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:48.691302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:48.971300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:49.220303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:49.461301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:49.712323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:50.082297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:50.330295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:50.567300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:50.843300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:51.156296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:51.417304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:51.660296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:51.890296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:52.150301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:52.409298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:52.642300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:52.887302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:53.264299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:53.583326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:53.819295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:54.191303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:54.547302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:54.835299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:55.262302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:55.501299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:55.727302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:56.117303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:56.372305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:56.616319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:56.848298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:57.107304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:57.457303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:57.687321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:57.961304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:58.257296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:58.565298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:58.907302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:59.149303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:59.429295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:59.666302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:16:59.936303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:00.192303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:00.495304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:00.829560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:01.077475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:01.462849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:01.703833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:01.940833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:02.181824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:02.438825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:02.668832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:02.884824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:03.104827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:03.338850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:03.561831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:03.890825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:04.146830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:04.466829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:04.724833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:05.032826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:05.258832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:05.493825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:05.718825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:05.936846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:06.192849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:06.424848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:06.672825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:06.952837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:07.215830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:07.469827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:07.707826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:07.922824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:08.177824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:08.411826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:08.653828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:08.884833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:09.111850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:09.357826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:09.590832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:09.837832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:10.100826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:10.410832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:10.651827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:10.892827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:11.210829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:11.482830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:11.750830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:12.027826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:12.806391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:13.313390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:13.808389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:14.248389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:14.675388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:15.112385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:15.460384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:15.955386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:16.280388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:16.543385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:16.856401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:17.189387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:17.521389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:17.897391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:18.174392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:18.473388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:18.716383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:19.009390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:19.291388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:19.559383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:19.934384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:20.568393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:21.025385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:21.313382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:21.731389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:22.124388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:22.520390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:22.784408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:23.163392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:23.479385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:24.214386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:24.746390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:25.242385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:25.534975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:25.797966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:26.082612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:26.423117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:26.654116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:26.903125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:27.136121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-21T13:17:42.798141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-21T13:17:43.513115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-21T13:17:44.061120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-21T13:17:44.518112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-21T13:17:44.947118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-21T13:17:27.651119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-21T13:17:28.317118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityquality_levelwine_type
007.00.270.3620.70.04545.0170.01.00103.000.458.86Goodwhite
116.30.300.341.60.04914.0132.00.99403.300.499.56Goodwhite
228.10.280.406.90.05030.097.00.99513.260.4410.16Goodwhite
337.20.230.328.50.05847.0186.00.99563.190.409.96Goodwhite
447.20.230.328.50.05847.0186.00.99563.190.409.96Goodwhite
558.10.280.406.90.05030.097.00.99513.260.4410.16Goodwhite
666.20.320.167.00.04530.0136.00.99493.180.479.66Goodwhite
777.00.270.3620.70.04545.0170.01.00103.000.458.86Goodwhite
886.30.300.341.60.04914.0132.00.99403.300.499.56Goodwhite
998.10.220.431.50.04428.0129.00.99383.220.4511.06Goodwhite

Last rows

df_indexfixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcoholqualityquality_levelwine_type
648715896.60.7250.207.80.07329.079.00.997703.290.549.25Goodred
648815906.30.5500.151.80.07726.035.00.993143.320.8211.66Goodred
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